The charts below represents the main findings of some recent analysis of 1,000 data scientist LinkedIn profiles, using a web scraper. It was limited to Singapore, and for people having "data scientist" on their profile. Of course, many have a different job title especially in fields such as Fintech (quant engineer) or Healthcare (biostatistician) but the findings are interesting nevertherless and seem to apply to other locales as well.

The first chart features the educational…

Introduction

Will china dominate AI?

When I spoke at the UK China business forum last month, I discussed this topic in response to an audience question.

In the current climate of nationalistic fervour, I see the same question asked in many guises.

For…

This article was written by Steeve Huang.

Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed reward in the next time step to evaluate its previous action. It was mostly used in games (e.g. Atari, Mario), with performance on par with or even exceeding humans. Recently, as the algorithm evolves with the combination of Neural Networks, it is capable…

This is an interesting data science conjecture, inspired by the well known six degrees of separation problem, stating that there is a link involving no more than 6 connections between any two people on Earth, say between you and anyone living (say) in North Korea.   

Here the link is between any two univariate data sets of the same…

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.

• Find your…

This is the next blog in my random series on better understanding some of these advanced Artificial Intelligence and Deep Learning algorithms. This “episode” takes on Generative Adversarial Networks (GANs). Hope you enjoy my “Deep Learning” learning journey.

I originally wrote in “Transforming from Autonomous to Smart: Reinforcement Learning Basics” how…

It is not unusual for a person to encounter hurdles or barriers in social processes - for example, to obtain financial support to do a post-secondary program.  I drink decaffeinated coffee. Sometimes when I order coffee, perhaps due to lack of product demand I will receive a cup of coffee that is barely above room temperature.  This situation likewise extends from some kind of social process - e.g. to get the order out although it is not quite what I ordered.…

Bayesian Machine Learning (part -5)

Introduction: Expectation-Maximization

In this blog we are going to see how Expectation-maximization algorithm works very closely. This blog is in strict continuation of the previous blog. Previously we saw how…

The material discussed here is also of interest to machine learning, AI, big data, and data science practitioners, as much of the work is based on heavy data processing, algorithms, efficient coding, testing, and experimentation. Also, it's not just two new conjectures, but paths and suggestions to solve these problems. The last section contains a few new, original exercises, some with solutions, and may be useful to students, researchers, and instructors offering math and statistics classes…

Statistics, Statistical Learning, and Machine Learning are three different areas with a large amount of overlap. Despite that overlap, they are distinct fields in their own right. The following picture illustrates the difference between the three fields.

References…

Interesting analysis done in R, about salaries of R developers broken down by country, featuring salary range and median salary. 

The dataset consists of survey answers from nearly 90,000 respondents. About 5,000 of them reported using R for “extensive development work over the past year”. The first filter used reduces the dataset from 88,883 respondents to 5,048. The second filter…

Despite the consuming controversy surrounding his presidency, POTUS 45 has been able to secure solid ratings on the performance of the economy over his so-far 30-month administration. And he certainly isn't bashful about taking credit for the successes, opining loudly and often that his tax cuts and de-regulation initiatives…

Summary:  99% of our application of NLP has to do with chatbots or translation.  This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels.  The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers.

 …

Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. Compute all the (99,999 * 100,000) / 2 cross-correlations. You are almost guaranteed to find one above 0.999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle…

Please join me in Las Vegas at Hitachi Vantara’s NEXT 2019 in October where I’ll be…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, …

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, and many more. To keep receiving these articles, …

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  

Featured Resources and Technical…

The positive reactions on my last post: “Different kinds of loops in R” lead me to compare some different versions of loops in R, RCPP (C++ integration of R). To see a bigger picture, I apply the Python for-loop additionally. The comparison focuses on the runtime for non-costly tasks with different numbers of iterations. For comparison purpose I create vectors in the form of (R syntax):

Vector <- 1:k

k = (1.000, 100.000, 1.000.000)

The task is to…